Mario Kart Wii

1. What research question(s) did you explore? Why are your questions(s) relevant?

We decided to focus on the question "what is the best character/vehicle combination in Mario Kart Wii?" This question is relevant because the community around the game is still active through community created servers, mods, and speedruns. The community seems to be unanimous in saying that Daisy on the Mach bike and Funky Kong on the Flame Runner are the best combinations. We wondered why that was and if there's substance behind those claims.

1. Who are your intended audience?

Our intended audience is anyone that plays Mario Kart Wii. Like we said, the community is still very active and the game has a considerable following both in Wegatz in general and Third East Wengatz specifically.

1. What data sets have you chosen to analyze/visualize? How did you choose the data sets? Why do you have confidence that these data will help answer your research questions?

We've chosen to analize vehicle and character statistics and characteristics as well as speed running data. We chose the character/vehicle data source because it would provide valuable insight into what advantages a particular character/vehicle might have. The data source is popular in the community and is frequently cited. Speedrunning data was used to show what combinations speedrunners were using. We figured that, since Mario Kart is a racing game, going fast and staying in control enough to maneuver around sudden obsticals and take opportunities (such as a dropped mushroom boost), would be the forte of the speedrunning community. Knowing what combinations of characters and vehicles the speedrunning community is using would be helpful both to show what is popular and to act as a loose guide of what we could expect the outcome to be.

Character/vehicle statistics data source: Kaggle

Speedrunning data source: Speedrun

Speedrunning data api: Github

Additional data source for drift information (entered manually): Gamefaqs

Mario Kart Wii characteristics explanation: Wordpress

1. What tools and techniques did you employ to visualize your data?

We mostly used plotly and pandas to work with and show data. We also used an api to acess speedrunning data. More specifically, we utilized filtering to cut out characters and vehicles that did not fit our minimum criteria for viability. This would allow us to create a pool of vehicles that are at least as close to Daisy/Mach Bike and Funky Kong/Flame Runner combinations. If this pool was every vehicle/character in the game, then we could reasonably say that either vehicle/character combinations don't matter as much or the aforementioned combinations are actually really poor combinations.

1. What types of visualizations did you produce? Why did you chose those particular visualizations?

We mostly stuck with simple bar charts for our visualizations, however, we also utilized some histogram graphics and a scatterplot. We chose to primarily stick with bar charts because they were able to show what we wanted the audience to see when we wanted them to see it. We went through a few different criteria filters to reach our conclusion, however, not all criteria is created equal. For example, we decided speed would be our primary criteria since that made the most amount of sense in a race. We thought that the audience might miss what we were doing at each stage of the filtering process if we included more than one of our filtering criteria at once before we reached our conclusion. We used a histogram to display popularity of character/vehicle combinations among the speedrunning community. We used a scatterplot to show vehicles graphed as a function of two of our most important criteria.

1. How did you choose your analysis and visualization strategy to communicate clearly and honestly to your audience?

We labeled as much as we could to keep misunderstandings to a minimum. We were worried that some of our early bar graphs from before our filtering criteria would make it hard for the audience to see the actual value associated with each bar. To get around this, we put the bar's value inside the bar. We were also worried that the audience would misunderstand what characters could drive which vehicles. Therefore, we color coded our bar graphs in a consistent way to make it clear what weight class each character and vehicle was in.

1. What impact do you hope your analysis and visualizations will have on your audience?

We hope that our audience comes away with a better understanding of the game, what criteria is important when selecting a character/vehicle and what some general trends among the vehicles are. For example, very fast vehicles don't typically have great acceleration.

import pandas as pd
import plotly.express as px

bikes = pd.read_csv('./data/bikes.csv')
characters = pd.read_csv('./data/characters.csv')
karts = pd.read_csv('./data/karts.csv')

bikes.rename(columns={'Bike':'Name'}, inplace=True)
bikes['Type'] = 'Bike'

karts.rename(columns={'Kart':'Name'}, inplace=True)
karts['Type'] = 'Kart'

characters

	Character	Class	Speed	Weight	Acceleration	Handling	Drift	Off-Road	Mini-Turbo
0	Baby Mario	Light	0	8	0	6	0	0	0
1	Baby Luigi	Light	5	8	0	0	0	0	0
2	Baby Peach	Light	3	6	3	3	0	0	0
3	Baby Daisy	Light	5	6	0	0	0	0	3
4	Toad	Light	0	0	6	0	6	0	0
5	Toadette	Light	3	0	0	0	0	6	0
6	Koopa Troopa	Light	0	0	0	3	0	0	6
7	Dry Bones	Light	0	0	3	0	3	0	3
8	Mii S	Light	3	3	0	0	3	0	3
9	Mario	Medium	0	6	2	2	3	0	0
10	Luigi	Medium	2	6	0	0	0	0	0
11	Peach	Medium	2	0	5	0	6	0	0
12	Daisy	Medium	4	0	0	2	0	0	3
13	Yoshi	Medium	0	3	0	0	3	5	0
14	Birdo	Medium	0	3	0	0	0	3	4
15	Diddy Kong	Medium	0	0	3	0	3	0	5
16	Bowser Jr.	Medium	0	0	0	0	0	3	3
17	Mii M	Medium	3	3	0	0	0	3	3
18	Wario	Heavy	0	3	0	0	0	3	6
19	Waluigi	Heavy	0	0	6	0	5	3	0
20	Donkey Kong	Heavy	0	3	2	2	0	0	3
21	Bowser	Heavy	2	5	0	0	3	0	0
22	King Boo	Heavy	0	0	0	5	0	3	0
23	Rosalina	Heavy	3	0	0	3	0	0	3
24	Funky Kong	Heavy	4	0	0	0	0	3	0
25	Dry Bowser	Heavy	0	0	0	0	0	6	6
26	Mii L	Heavy	3	0	3	3	3	0	0

vehicles = pd.concat([karts, bikes])
vehicles

	Name	Class	Speed	Weight	Acceleration	Handling	Drift	Off-Road	Mini-Turbo	Type
0	Standard Kart S	Light	41	29	48	48	51	30	45	Kart
1	Booster Seat	Light	27	27	56	64	37	54	59	Kart
2	Mni Beast	Light	55	32	29	32	64	27	64	Kart
3	Cheep Charger	Light	34	24	64	56	59	45	54	Kart
4	Tiny Titan	Light	46	35	43	43	29	64	40	Kart
5	Blue Falcon	Light	60	29	35	29	43	24	29	Kart
6	Standard Kart M	Medium	46	45	40	43	45	35	40	Kart
7	Classic Dragster	Medium	37	43	59	54	54	40	51	Kart
8	Wild Wing	Medium	57	51	21	29	59	24	59	Kart
9	Super Blooper	Medium	50	40	35	37	21	54	35	Kart
10	Daytripper	Medium	34	45	51	59	32	48	54	Kart
11	Sprinter	Medium	64	48	27	24	37	21	24	Kart
12	Standard Kart L	Heavy	48	59	37	40	40	35	35	Kart
13	Offroader	Heavy	39	64	48	54	18	43	45	Kart
14	Flame Flyer	Heavy	62	59	16	21	48	18	48	Kart
15	Pirhanha Prowler	Heavy	55	67	29	35	35	29	27	Kart
16	Jetsetter	Heavy	69	56	21	17	27	16	16	Kart
17	Honeycoupe	Heavy	53	62	17	19	56	24	56	Kart
0	Standard Bike S	Light	39	21	51	51	54	43	48	Bike
1	Bullet Bike	Light	53	24	32	35	67	29	67	Bike
2	Bit Bike	Light	25	18	59	67	40	56	62	Bike
3	Quacker	Light	32	17	67	60	63	48	57	Bike
4	Magikruiser	Light	43	24	45	45	32	67	43	Bike
5	Jet Bubble	Light	48	27	40	40	45	35	37	Bike
6	Standard Bike M	Medium	46	45	40	43	45	35	40	Bike
7	Mach Bike	Medium	55	37	24	32	62	27	62	Bike
8	Sugarscoot	Medium	32	32	54	62	35	51	56	Bike
9	Zip Zip	Medium	41	35	45	51	29	62	45	Bike
10	Sneakster	Medium	62	40	29	27	40	24	27	Bike
11	Dolphin Dasher	Medium	48	43	37	40	24	56	37	Bike
12	Standard Bike L	Heavy	46	54	40	43	43	37	37	Bike
13	Flame Runner	Heavy	60	54	18	24	51	21	51	Bike
14	Wario Bike	Heavy	37	59	51	56	21	45	48	Bike
15	Shooting Star	Heavy	50	48	29	32	59	27	59	Bike
16	Spear	Heavy	67	56	24	18	29	18	18	Bike
17	Phantom	Heavy	43	51	43	48	17	56	40	Bike

bar = px.bar(vehicles, x='Name', y='Speed', color='Class', text_auto='.2s', title='Speeds of Vehicles Grouped by Weight Class')
bar.update_yaxes(showticklabels=True, tickmode = 'array', tickvals=[0, 20, 40, 60, 80])
bar.show()

filtered_vehicles = vehicles.loc[vehicles['Speed'] >= 50]
bar = px.bar(filtered_vehicles, x='Name', y='Speed', color='Class', text_auto='.2s', title='Speeds of Viable Vehicles Grouped by Weight Class')
bar.update_yaxes(showticklabels=True, tickmode = 'array', tickvals=[0, 20, 40, 60, 80])
bar.show()

bar = px.bar(characters, x='Character', y='Speed', color='Class', text_auto='.2s', title='Speed Modifications of Characters Grouped by Weight Class')
bar.show()

bar = px.bar(filtered_vehicles, x='Name', y='Drift', color='Class', text_auto='.2s', title='Drift Stats of Vehicles Grouped by Weight Class')
bar.show()

bar = px.bar(characters, x='Character', y='Drift', color='Class', text_auto='.2s', title='Drift Modifications of Characters Grouped by Weight Class')
bar.show()

filtered_vehicles = filtered_vehicles.loc[filtered_vehicles['Drift'] >= 45]
bar = px.bar(filtered_vehicles, x='Name', y='Speed', color='Class', text_auto='.2s', title='Speed Stats of Vehicles Grouped by Weight Class')

viable = filtered_vehicles.sort_values(by=['Class', 'Speed', 'Drift', 'Mini-Turbo'], ascending=False).reset_index().drop(columns=['index'])

viable['Drift_Type'] = 'Outside'
for i in [1, 3, 5]:
    viable.iat[i, viable.columns.get_loc('Drift_Type')] = 'Inside'
viable

	Name	Class	Speed	Weight	Acceleration	Handling	Drift	Off-Road	Mini-Turbo	Type	Drift_Type
0	Wild Wing	Medium	57	51	21	29	59	24	59	Kart	Outside
1	Mach Bike	Medium	55	37	24	32	62	27	62	Bike	Inside
2	Mni Beast	Light	55	32	29	32	64	27	64	Kart	Outside
3	Bullet Bike	Light	53	24	32	35	67	29	67	Bike	Inside
4	Flame Flyer	Heavy	62	59	16	21	48	18	48	Kart	Outside
5	Flame Runner	Heavy	60	54	18	24	51	21	51	Bike	Inside
6	Honeycoupe	Heavy	53	62	17	19	56	24	56	Kart	Outside
7	Shooting Star	Heavy	50	48	29	32	59	27	59	Bike	Outside

fig = px.scatter(viable, y='Speed', x='Drift', color='Drift_Type', text='Name', title='Speed and Drift of Viable Vehicles Grouped by Drift Type')
fig.update_traces(textposition='bottom center')
fig.show()